IEEE Trans Image Process - Visual classification with multitask joint sparse representation.

Tópicos

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Resumo

We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. The proposed model can be efficiently optimized by a proximal gradient method. Furthermore, we extend our method to the setup where features are described in kernel matrices. We then investigate into two applications of our method to visual classification: 1) fusing multiple kernel features for object categorization and 2) robust face recognition in video with an ensemble of query images. Extensive experiments on challenging real-world data sets demonstrate that the proposed method is competitive to the state-of-the-art methods in respective applications.

Resumo Limpo

address problem visual classif multipl featur andor multipl instanc motiv recent success multitask joint covari select formul problem multitask joint spars represent model combin strength multipl featur andor instanc recognit joint sparsityinduc norm util enforc classlevel joint sparsiti pattern among multipl represent vector propos model can effici optim proxim gradient method furthermor extend method setup featur describ kernel matric investig two applic method visual classif fuse multipl kernel featur object categor robust face recognit video ensembl queri imag extens experi challeng realworld data set demonstr propos method competit stateoftheart method respect applic

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